Predicting Stenosis Aggravation in Follow-Up High-Resolution Magnetic Resonance Images of Patients with Intracranial Atherosclerosis.

BACKGROUND: High-resolution magnetic resonance imaging (HRMRI) can provide information on the histopathological characteristics of intracranial atherosclerotic lesions causing arterial stenosis; however, its clinical application in intracranial atherosclerosis lacks standardization for predicting stenosis. Therefore, this study investigated the characteristics of HRMRI that can predict progression based on comparisons of follow-up HRMRI. METHODS: We retrospectively enrolled patients who underwent HRMRI within 7 days of symptom onset to evaluate the characteristics associated with intracranial stenotic lesions. Among them, patients diagnosed with severe stenosis due to atherosclerosis and who underwent follow-up HRMRI 12-24 months after initial HRMRI were included in the final study. We analyzed distinct features, such as stenosis aggravation, the presence of initial plaque enhancement, increment of plaque enhancement, the existence of both eccentric and concentric plaques, and the presence of initial intraplaque hematoma on initial and follow-up HRMRI. RESULTS: Among 442 patients who underwent HRMRI for severe stenosis due to atherosclerosis, 35 underwent follow-up HRMRI 12-24 months later. Patients with stenosis aggravation showed a higher incidence of plaque enhancement (87.5% vs. 3.7%, p < 0.001) and the presence of both concentric and eccentric plaques (75.0% vs. 11.1%; p = 0.001). The area under the curve for the increment of plaque enhancement was 0.92 (95% confidence interval [CI] 0.78-1.00, p ≤ 0.001), while that for the presence of both concentric and eccentric plaques was 0.82 (95% CI 0.63-1.00, p < 0.007). CONCLUSIONS: The presence of both concentric and eccentric plaques and an increase in plaque enhancement were the strongest predictors of aggravation of intracranial artery stenosis.

Converting from the Montreal Cognitive Assessment to the Mini-Mental State Examination-2.

OBJECTIVE: The Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination-2 (MMSE-2) are useful psychometric tests for cognitive screening. Many clinicians want to predict the MMSE-2 score based on the MoCA score. To facilitate the transition from the MoCA to the MMSE-2, this study developed a conversion method. METHODS: This study retrospectively examined the relationship between the MoCA and MMSE-2. Overall, 303 participants were evaluated. We produced a conversion table using the equipercentile equating method with log-linear smoothing. Then, we evaluated the reliability and accuracy of this algorithm to convert the MoCA to the MMSE-2. RESULTS: MoCA scores were converted to MMSE-2 scores according to a conversion table that achieved a reliability of 0.961 (intraclass correlation). The accuracy of this algorithm was 84.5% within 3 points difference from the raw score. CONCLUSIONS: This study reports a reliable and easy conversion algorithm for transforming MoCA scores into converted MMSE-2 scores. This method will greatly enhance the utility of existing cognitive data in clinical and research settings.